Research on Cloud Computing Industrial Cluster Innovation Evaluation Model

2013 ◽  
Vol 711 ◽  
pp. 647-651
Author(s):  
Yi Jie Zhuang ◽  
Min Wang ◽  
Xiao Chong Pan
Keyword(s):  
Cloud Computing ◽  
Bayesian Network ◽  
Evaluation Model ◽  
Measurement Model ◽  
Industrial Clusters ◽  
Assessment Model ◽  
Innovation Measurement ◽  
Computing Industry ◽  
Key Variables ◽  
Self Learning

In this paper, a Bayesian network-based assessment model used for evaluating the innovation of cloud computing industry is presented. Firstly, the innovation measurement model of cloud computing industrial clusters is designed. Then Bayesian network assessment and the self-learning method to the model are proposed. Finally, accompanying with empirical data, the most likely innovation status value of cloud computing industrial clusters and key variables influencing the innovation status value can be predicted. This model can provide the theory basis for researching the innovative development of cloud computing industries.

Industrial Cluster Competitiveness Evaluation Model Research Based on Entropy Weight TOPSIS Method

2014 ◽  
Vol 584-586 ◽  
pp. 2676-2680 ◽  
Author(s):  
Yuan Yuan Zhang ◽  
Fu Zhou Luo
Keyword(s):  
Evaluation Model ◽  
Core Competencies ◽  
Ferrous Metal ◽  
Industrial Clusters ◽  
Evaluation Index System ◽  
Assessment Model ◽  
Shaanxi Province ◽  
Evaluation Index ◽  
Topsis Method ◽  
Entropy Weight

Existing competitiveness evaluation methods of industrial clusters is too subjective and can’t be a true reflection of its core competencies; evaluation index is not uniform and can’t form a competitiveness evaluation index system. We took non-ferrous metal industry cluster of Shaanxi Province as an example, built a competitive assessment model of industrial clusters from scale, market, innovation, and efficiency competitiveness. We used Entropy-TOPSIS method to analyze. The results show that Entropy-TOPSIS method is more objective and matches the actual development in the evaluation of industrial clusters competitiveness.

Supply Chain Risk Evaluation Based on Bayesian Network

2013 ◽  
Vol 680 ◽  
pp. 550-553
Author(s):  
Bo Chao Liu
Keyword(s):  
Risk Assessment ◽  
Supply Chain ◽  
Bayesian Network ◽  
Risk Evaluation ◽  
Evaluation Model ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Supply Chain Risk ◽  
Logistics Company ◽  
Proposed Model

The evaluation for supply chain risk is very important to show the latent risk and eliminate the risk. In the study, Bayesian network is proposed to evaluate the supply chain risk. The assessment indexes of supply chain risk are analyzed before supply chain risk assessment. Then, the assessment indexes of supply chain risk can be used to construct the supply chain risk assessment model. We apply a certain logistics company to study the evaluation ability of Bayesian network evaluation model proposed here. The experimental results prove the effectiveness of the proposed model.

Industry 4.0: The Importance Factors of Innovation in Adopting Cloud Computing Among SMES in Malaysia

2020 ◽  
Vol 11 (1) ◽  
pp. 84-84
Author(s):  
Siti Salwa Sheikh Mokhtar ◽  
Anuar Shah Bali Mahomed ◽  
Yuhanis Abdul Aziz ◽  
Suhaimi Ab. Rahman
Keyword(s):  
Cloud Computing ◽  
Technology Acceptance ◽  
Industry 4.0 ◽  
Structural Equation ◽  
Industrial Revolution ◽  
Equation Model ◽  
Computing Industry ◽  
Global Economic Growth ◽  
Acceptance Model ◽  
Third Industrial Revolution

Small and medium-sized enterprises (SMEs) are commonly perceived as an essential part of boosting and stabilizing global economic growth. In 2018, SMEs recorded a 38.3% contribution to GDP of RM521.7 billion compared to RM491.2 billion in 2017. SMEs are expected to contribute 50% to Malaysia's GDP by 2030 relative to its present 38% contribution. However, in the context of Malaysia businesses, particularly small and medium-sized enterprises, are still not embracing the latest technology revolution sufficiently, as reported by the Ministry of International Trade and Industry (2018). Malaysia is currently in the third industrial revolution (automation), and some are still under the second industrial revolution. Such matter is worrying as only a few industries in Malaysia can adopt pillars of Industry 4.0, where business owners in Malaysia were still hesitant to embrace technologies such as the cloud. To bridge the gap in this analysis, this research adopted the technology acceptance model developed by Davis (1989) and Rogers' Diffusion Innovation Theory (1995), which incorporates the contexts of technology and innovation among SMEs in Malaysia. By using survey questionnaires, data was collected among manufacturing and services SMEs in Malaysia. Structural equation model employed to assess the important factors of innovation in adopting cloud computing among SMEs in Malaysia by using Smart-PLS. Keywords: Cloud computing, Industry 4.0, Innovation, Technological

A Bayesian-Network-based Approach to Risk Analysis in Runway Excursions

2019 ◽  
Vol 72 (5) ◽  
pp. 1121-1139 ◽  
Author(s):  
Fernando Calle-Alonso ◽  
Carlos J. Pérez ◽  
Eduardo S. Ayra
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
Exploratory Analysis ◽  
Hypothesis Tests ◽  
Aircraft Accidents ◽  
Risk Scenarios ◽  
Key Variables ◽  
Expert Recommendations

Aircraft accidents are extremely rare in the aviation sector. However, their consequences can be very dramatic. One of the most important problems is runway excursions, when an aircraft exceeds the end (overrun) or the side (veer-off) of the runway. After performing exploratory analysis and hypothesis tests, a Bayesian-network-based approach was considered to provide information from risk scenarios involving landing procedures. The method was applied to a real database containing key variables related to landing operations on three runways. The objective was to analyse the effects over runway overrun excursions of failing to fulfil expert recommendations upon landing. For this purpose, the most influential variables were analysed statistically, and several scenarios were built, leading to a runway ranking based on the risk assessed.

Reinforcement Learning for Cloud Computing Digital Library

2014 ◽  
Vol 571-572 ◽  
pp. 105-108
Author(s):  
Lin Xu
Keyword(s):  
Artificial Intelligence ◽  
Cloud Computing ◽  
Reinforcement Learning ◽  
Digital Library ◽  
Learning Algorithms ◽  
Experimental Results ◽  
Current Status ◽  
Self Learning ◽  
New Framework

This paper proposes a new framework of combining reinforcement learning with cloud computing digital library. Unified self-learning algorithms, which includes reinforcement learning, artificial intelligence and etc, have led to many essential advances. Given the current status of highly-available models, analysts urgently desire the deployment of write-ahead logging. In this paper we examine how DNS can be applied to the investigation of superblocks, and introduce the reinforcement learning to improve the quality of current cloud computing digital library. The experimental results show that the method works more efficiency.

Research on Combat Effectiveness Evaluation of Radar EW System Based on Bayesian Network

2011 ◽  
Vol 204-210 ◽  
pp. 1697-1700 ◽  
Author(s):  
Yu Jie Zheng
Keyword(s):  
Bayesian Network ◽  
Evaluation Model ◽  
Effectiveness Evaluation ◽  
Analysis Model ◽  
Key Factors ◽  
Bayesian Network Model ◽  
Simulation Data ◽  
Factors Analysis ◽  
Combat Effectiveness ◽  
Hidden Knowledge

Radar EW system combat effectiveness evaluation is a essential link to Radar system Demonstration, mainly give service to selection, optimization and key factors analysis of Weapon equipment scheme. In this paper, we introduce the Bayesian network model into the area of Radar EW system combat effectiveness evaluation and put forward the concept of combat effectiveness evaluation model based on Bayesian network. The ability to express complex relationship, the ability to express the uncertainty of probability, and the reasoning functions. By learning from Expertise and Simulation data, excavating the hidden knowledge included in both of them, we can build the combat efficiency Analysis model, and then carry out efficient analysis.

Application of Bayesian for the Situation Assessment of Sea-Battlefield

2013 ◽  
Vol 346 ◽  
pp. 135-139 ◽  
Author(s):  
Yong Tao Yu ◽  
Ying Ding ◽  
Zheng Xi Ding
Keyword(s):  
Decision Support ◽  
Bayesian Networks ◽  
Bayesian Network ◽  
Conditional Probability ◽  
Assessment Model ◽  
Situation Assessment ◽  
Network Characteristics ◽  
Specific Assessment

The sea-battlefield situation is dynamic and how efficient sea-battlefield situation assessment is a major problem facing operational decision support. According to research based on Bayesian networks Sea-battlefield situation assessment, first constructed sea-battlefield situation assessment Bayesian network; followed by specific assessment objectives, to simplify creating sub Bayesian assessment model; once again based on Bayesian network characteristics to determine each node probability formula; finally, according to the formula for solving the edge of the probability and the conditional probability of each node, sea-battlefield situation assessment.

Establishment of Risk Assessment Model for Ship Collision using Applied Bayesian Network -Collision Accidents between Fishing vessels and Cargo Vessels -

2015 ◽  
Author(s):  
Takeshi Shinoda ◽  
Koji Uru
Keyword(s):  
Risk Assessment ◽  
Bayesian Network ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Assessment Model ◽  
Risk Assessment Model ◽  
Benefit Analysis ◽  
Fishing Vessels ◽  
Formal Safety Assessment ◽  
Trial Cost

In this study, a risk assessment model for ship collisions is proposed according to the guidelines for Formal Safety Assessment (FSA) approved by IMO in 2002. The analysis is applied to ship collisions between fishing and cargo vessels owing to their high frequency and enormous damage. Bayesian network theory for risk analysis has been applied to reveal a causal relationship on human factors. A trial evaluation of Risk Control Options (RCOs) for collisions is attempted through the calculation of the dominance index. Finally, a trial cost benefit analysis for RCOs is considered through Gross Cost of Averting Fatality (GCAF) in FSA.

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